def testInstancesOfOneClassOnly(self):
# Setup test data with 1 positive (ignored), and 1 negative example.
example_protos = [
make_example_proto(
{'age': [0],
'gender': [0]}, 0),
make_example_proto(
{'age': [1],
'gender': [0]}, 1), # Shares gender with the instance above.
]
example_weights = [1.0, 0.0] # Second example "omitted" from training.
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=0.25,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
tf.initialize_all_variables().run()
lr = SdcaModel(CONTAINER, examples, variables, options)
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
prediction = lr.predictions(examples)
lr.minimize().run()
self.assertAllClose(0.395226,
unregularized_loss.eval(),
rtol=3e-2,
atol=3e-2)
self.assertAllClose(0.460781, loss.eval(), rtol=3e-2, atol=3e-2)
predicted_labels = tf.cast(
tf.greater_equal(prediction,
tf.ones_like(prediction) * 0.5), tf.float32)
self.assertAllEqual([0, 0], predicted_labels.eval())
def testHingeDenseFeaturesWeightedExamples(self):
with self._single_threaded_test_session():
examples = make_dense_examples_dict(
dense_feature_values=[[1.0, 1.0], [0.5, -0.5]],
weights=[3.0, 1.0],
labels=[1.0, 0.0])
variables = make_dense_variable_dict(2, 2)
options = dict(symmetric_l2_regularization=1.0,
symmetric_l1_regularization=0,
loss_type='hinge_loss')
tf.initialize_all_variables().run()
model = SdcaModel(CONTAINER, examples, variables, options)
predictions = model.predictions(examples)
binary_predictions = get_binary_predictions_for_hinge(predictions)
model.minimize().run()
# Point (1.0, 0.5) has higher weight than (1.0, -0.5) so the model will
# try to increase the margin from (1.0, 0.5). Due to regularization,
# (1.0, -0.5) will be within the margin. For these points and example
# weights, the optimal weights are w_1~=0.4 and w_2~=1.2 which give an L2
# loss of 0.25 * 1.6 = 0.4. The binary predictions will be correct, but
# the boundary will be much closer to the 2nd point than the first one.
self.assertAllClose([1.0, -0.2], predictions.eval(), atol=0.05)
self.assertAllClose([1.0, 0.0], binary_predictions.eval(), atol=0.05)
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
self.assertAllClose(0.2, unregularized_loss.eval(), atol=0.02)
self.assertAllClose(0.6, regularized_loss.eval(), atol=0.02)
def testInstancesOfOneClassOnly(self):
# Setup test data with 1 positive (ignored), and 1 negative example.
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [0]
}, 1), # Shares gender with the instance above.
]
example_weights = [1.0, 0.0] # Second example "omitted" from training.
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
lr = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
for _ in xrange(5):
lr.minimize().run()
self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.12)
self.assertAllClose(0.525457, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 0], predicted_labels.eval())
self.assertAllClose(0.01,
lr.approximate_duality_gap().eval(),
rtol=1e-2,
atol=1e-2)
def testSimple(self):
# Setup test data
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [1.0, 1.0]
with self.test_session(use_gpu=False):
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(examples, 1, 1)
options = dict(symmetric_l2_regularization=0.5,
symmetric_l1_regularization=0,
loss_type='logistic_loss',
prior=0.0)
tf.initialize_all_variables().run()
lr = SdcaModel(examples, variables, options)
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
prediction = lr.predictions(examples)
self.assertAllClose(0.693147, unregularized_loss.eval())
self.assertAllClose(0.693147, loss.eval())
lr.minimize().run()
self.assertAllClose(0.395226, unregularized_loss.eval())
self.assertAllClose(0.657446, loss.eval())
predicted_labels = tf.cast(
tf.greater_equal(prediction,
tf.ones_like(prediction) * 0.5), tf.float32)
self.assertAllEqual([0, 1], predicted_labels.eval())
def testImbalancedWithExampleWeights(self):
# Setup test data with 1 positive, and 3 negative examples.
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [3.0, 1.0]
with self.test_session(use_gpu=False):
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(examples, 1, 1)
options = dict(symmetric_l2_regularization=0.25,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
tf.initialize_all_variables().run()
lr = SdcaModel(examples, variables, options)
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
prediction = lr.predictions(examples)
lr.minimize().run()
self.assertAllClose(0.266189,
unregularized_loss.eval(),
rtol=3e-2,
atol=3e-2)
self.assertAllClose(0.571912, loss.eval(), rtol=3e-2, atol=3e-2)
predicted_labels = tf.cast(
tf.greater_equal(prediction,
tf.ones_like(prediction) * 0.5), tf.float32)
self.assertAllEqual([0, 1], predicted_labels.eval())
def testDenseFeaturesWeightedExamples(self):
with self._single_threaded_test_session():
examples, variables = make_dense_examples_and_variables_dicts(
dense_features_values=[[[1.0], [1.0]], [[0.5], [-0.5]]], weights=[3.0, 1.0], labels=[1.0, 0.0]
)
options = dict(symmetric_l2_regularization=1.0, symmetric_l1_regularization=0, loss_type="hinge_loss")
model = SdcaModel(examples, variables, options)
tf.initialize_all_variables().run()
predictions = model.predictions(examples)
binary_predictions = get_binary_predictions_for_hinge(predictions)
train_op = model.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
# Point (1.0, 0.5) has higher weight than (1.0, -0.5) so the model will
# try to increase the margin from (1.0, 0.5). Due to regularization,
# (1.0, -0.5) will be within the margin. For these points and example
# weights, the optimal weights are w_1~=0.4 and w_2~=1.2 which give an L2
# loss of 0.5 * 0.25 * 0.25 * 1.6 = 0.2. The binary predictions will be
# correct, but the boundary will be much closer to the 2nd point than the
# first one.
self.assertAllClose([1.0, -0.2], predictions.eval(), atol=0.05)
self.assertAllEqual([1, 0], binary_predictions.eval())
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
self.assertAllClose(0.2, unregularized_loss.eval(), atol=0.02)
self.assertAllClose(0.4, regularized_loss.eval(), atol=0.02)
def testImbalancedWithExampleWeights(self):
# Setup test data with 1 positive, and 1 negative example.
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [3.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
lr = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
for _ in xrange(5):
lr.minimize().run()
self.assertAllClose(0.284860, unregularized_loss.eval(), rtol=0.08)
self.assertAllClose(0.408044, loss.eval(), atol=0.012)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertAllClose(0.01,
lr.approximate_duality_gap().eval(),
rtol=1e-2,
atol=1e-2)
def testImbalancedWithExampleWeights(self):
# Setup test data with 1 positive, and 3 negative examples.
example_protos = [
make_example_proto(
{'age': [0],
'gender': [0]}, 0),
make_example_proto(
{'age': [1],
'gender': [1]}, 1),
]
example_weights = [3.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=0.25,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
tf.initialize_all_variables().run()
lr = SdcaModel(CONTAINER, examples, variables, options)
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
prediction = lr.predictions(examples)
lr.minimize().run()
self.assertAllClose(0.266189, unregularized_loss.eval(),
rtol=3e-2, atol=3e-2)
self.assertAllClose(0.571912, loss.eval(), rtol=3e-2, atol=3e-2)
predicted_labels = tf.cast(
tf.greater_equal(prediction,
tf.ones_like(prediction) * 0.5), tf.float32)
self.assertAllEqual([0, 1], predicted_labels.eval())
def testSimpleLogistic(self):
# Setup test data
example_protos = [
make_example_proto(
{'age': [0],
'gender': [0]}, 0),
make_example_proto(
{'age': [1],
'gender': [1]}, 1),
]
example_weights = [1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=0.5,
symmetric_l1_regularization=0,
loss_type='logistic_loss',
prior=0.0)
tf.initialize_all_variables().run()
lr = SdcaModel(CONTAINER, examples, variables, options)
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
prediction = lr.predictions(examples)
self.assertAllClose(0.693147, unregularized_loss.eval())
self.assertAllClose(0.693147, loss.eval())
lr.minimize().run()
self.assertAllClose(0.395226, unregularized_loss.eval(),
rtol=3e-2, atol=3e-2)
self.assertAllClose(0.657446, loss.eval(),
rtol=3e-2, atol=3e-2)
predicted_labels = tf.cast(
tf.greater_equal(prediction,
tf.ones_like(prediction) * 0.5), tf.float32)
self.assertAllEqual([0, 1], predicted_labels.eval())
def testImbalancedWithExampleWeights(self):
# Setup test data with 1 positive, and 1 negative example.
example_protos = [
make_example_proto(
{'age': [0],
'gender': [0]}, 0),
make_example_proto(
{'age': [1],
'gender': [1]}, 1),
]
example_weights = [3.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
lr = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
for _ in xrange(5):
lr.minimize().run()
self.assertAllClose(0.284860, unregularized_loss.eval(), rtol=0.08)
self.assertAllClose(0.408044, loss.eval(), atol=0.012)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertAllClose(0.01,
lr.approximate_duality_gap().eval(),
rtol=1e-2,
atol=1e-2)
def testImbalanced(self):
# Setup test data with 1 positive, and 3 negative examples.
example_protos = [
make_example_proto({"age": [0], "gender": [0]}, 0),
make_example_proto({"age": [2], "gender": [0]}, 0),
make_example_proto({"age": [3], "gender": [0]}, 0),
make_example_proto({"age": [1], "gender": [1]}, 1),
]
example_weights = [1.0, 1.0, 1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(3, 1)
options = dict(
symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss", prior=-1.09861
)
tf.initialize_all_variables().run()
lr = SdcaModel(CONTAINER, examples, variables, options)
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
prediction = lr.predictions(examples)
lr.minimize().run()
self.assertAllClose(0.331710, unregularized_loss.eval(), rtol=3e-2, atol=3e-2)
self.assertAllClose(0.591295, loss.eval(), rtol=3e-2, atol=3e-2)
predicted_labels = tf.cast(tf.greater_equal(prediction, tf.ones_like(prediction) * 0.5), tf.float32)
self.assertAllEqual([0, 0, 0, 1], predicted_labels.eval())
def testSimple(self):
# Setup test data
example_protos = [
make_example_proto({"age": [0], "gender": [0]}, 0),
make_example_proto({"age": [1], "gender": [1]}, 1),
]
example_weights = [1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss")
lr = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
self.assertAllClose(0.693147, unregularized_loss.eval())
self.assertAllClose(0.693147, loss.eval())
train_op = lr.minimize()
for _ in xrange(_MAX_ITERATIONS):
train_op.run()
# The high tolerance in unregularized_loss comparisons is due to the
# fact that it's possible to trade off unregularized_loss vs.
# regularization and still have a sum that is quite close to the
# optimal regularized_loss value. SDCA's duality gap only ensures that
# the regularized_loss is within 0.01 of optimal.
# 0.525457 is the optimal regularized_loss.
# 0.411608 is the unregularized_loss at that optimum.
self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05)
self.assertAllClose(0.525457, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testImbalanced(self):
# Setup test data with 1 positive, and 3 negative examples.
example_protos = [
make_example_proto({"age": [0], "gender": [0]}, 0),
make_example_proto({"age": [2], "gender": [0]}, 0),
make_example_proto({"age": [3], "gender": [0]}, 0),
make_example_proto({"age": [1], "gender": [1]}, 1),
]
example_weights = [1.0, 1.0, 1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(3, 1)
options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss")
lr = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
train_op = lr.minimize()
for _ in xrange(_MAX_ITERATIONS):
train_op.run()
self.assertAllClose(0.226487 + 0.102902, unregularized_loss.eval(), atol=0.08)
self.assertAllClose(0.328394 + 0.131364, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 0, 0, 1], predicted_labels.eval())
self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testImbalancedWithExampleWeights(self):
# Setup test data with 1 positive, and 1 negative example.
example_protos = [
make_example_proto({"age": [0], "gender": [0]}, 0),
make_example_proto({"age": [1], "gender": [1]}, 1),
]
example_weights = [3.0, 1.0]
for num_shards in _SHARD_NUMBERS:
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(
symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
num_table_shards=num_shards,
loss_type="logistic_loss",
)
lr = SdcaModel(examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
train_op = lr.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
self.assertAllClose(0.284860, unregularized_loss.eval(), atol=0.08)
self.assertAllClose(0.408044, loss.eval(), atol=0.012)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertAllClose(0.0, lr.approximate_duality_gap().eval(), rtol=2e-2, atol=1e-2)
def testDenseFeaturesPerfectlySeparable(self):
with self._single_threaded_test_session():
examples, variables = make_dense_examples_and_variables_dicts(
dense_features_values=[[1.0, 1.0], [1.0, -1.0]], weights=[1.0, 1.0], labels=[1.0, 0.0]
)
options = dict(symmetric_l2_regularization=1.0, symmetric_l1_regularization=0, loss_type="hinge_loss")
model = SdcaModel(examples, variables, options)
tf.initialize_all_variables().run()
predictions = model.predictions(examples)
binary_predictions = get_binary_predictions_for_hinge(predictions)
train_op = model.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
self.assertAllClose([1.0, -1.0], predictions.eval(), atol=0.05)
self.assertAllEqual([1, 0], binary_predictions.eval())
# (1.0, 1.0) and (1.0, -1.0) are perfectly separable by x-axis (that is,
# the SVM's functional margin >=1), so the unregularized loss is ~0.0.
# There is only loss due to l2-regularization. For these datapoints, it
# turns out that w_1~=0.0 and w_2~=1.0 which means that l2 loss is ~0.25.
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
self.assertAllClose(0.0, unregularized_loss.eval(), atol=0.02)
self.assertAllClose(0.25, regularized_loss.eval(), atol=0.02)
def testInstancesOfOneClassOnly(self):
# Setup test data with 1 positive (ignored), and 1 negative example.
example_protos = [
make_example_proto({"age": [0], "gender": [0]}, 0),
make_example_proto({"age": [1], "gender": [0]}, 1), # Shares gender with the instance above.
]
example_weights = [1.0, 0.0] # Second example "omitted" from training.
for num_shards in _SHARD_NUMBERS:
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(
symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
num_table_shards=num_shards,
loss_type="logistic_loss",
)
lr = SdcaModel(examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
train_op = lr.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05)
self.assertAllClose(0.525457, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 0], predicted_labels.eval())
self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testSimpleNoL2(self):
# Same as test above (so comments from above apply) but without an L2.
# The algorithm should behave as if we have an L2 of 1 in optimization but
# 0 in regularized_loss.
example_protos = [
make_example_proto({"age": [0], "gender": [0]}, 0),
make_example_proto({"age": [1], "gender": [1]}, 1),
]
example_weights = [1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=0, symmetric_l1_regularization=0, loss_type="logistic_loss")
lr = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
self.assertAllClose(0.693147, unregularized_loss.eval())
self.assertAllClose(0.693147, loss.eval())
train_op = lr.minimize()
for _ in xrange(_MAX_ITERATIONS):
train_op.run()
# There is neither L1 nor L2 loss, so regularized and unregularized losses
# should be exactly the same.
self.assertAllClose(0.40244, unregularized_loss.eval(), atol=0.01)
self.assertAllClose(0.40244, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testSomeUnweightedExamples(self):
# Setup test data with 4 examples, but should produce the same
# results as testSimple.
example_protos = [
# Will be used.
make_example_proto({"age": [0], "gender": [0]}, 0),
# Will be ignored.
make_example_proto({"age": [1], "gender": [0]}, 0),
# Will be used.
make_example_proto({"age": [1], "gender": [1]}, 1),
# Will be ignored.
make_example_proto({"age": [1], "gender": [0]}, 1),
]
example_weights = [1.0, 0.0, 1.0, 0.0]
with self._single_threaded_test_session():
# Only use examples 0 and 2
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss")
lr = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
train_op = lr.minimize()
for _ in xrange(_MAX_ITERATIONS):
train_op.run()
self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05)
self.assertAllClose(0.525457, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllClose([0, 1, 1, 1], predicted_labels.eval())
self.assertAllClose(0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testInstancesOfOneClassOnly(self):
# Setup test data with 1 positive (ignored), and 1 negative example.
example_protos = [
make_example_proto(
{'age': [0],
'gender': [0]}, 0),
make_example_proto(
{'age': [1],
'gender': [0]}, 1), # Shares gender with the instance above.
]
example_weights = [1.0, 0.0] # Second example "omitted" from training.
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
lr = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
for _ in xrange(5):
lr.minimize().run()
self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.12)
self.assertAllClose(0.525457, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 0], predicted_labels.eval())
self.assertAllClose(0.01,
lr.approximate_duality_gap().eval(),
rtol=1e-2,
atol=1e-2)
def testSomeUnweightedExamples(self):
# Setup test data with 4 examples, but should produce the same
# results as testSimple.
example_protos = [
# Will be used.
make_example_proto({"age": [0], "gender": [0]}, 0),
# Will be ignored.
make_example_proto({"age": [1], "gender": [0]}, 0),
# Will be used.
make_example_proto({"age": [1], "gender": [1]}, 1),
# Will be ignored.
make_example_proto({"age": [1], "gender": [0]}, 1),
]
example_weights = [1.0, 0.0, 1.0, 0.0]
with self._single_threaded_test_session():
# Only use examples 0 and 2
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1, symmetric_l1_regularization=0, loss_type="logistic_loss")
tf.initialize_all_variables().run()
lr = SdcaModel(CONTAINER, examples, variables, options)
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
prediction = lr.predictions(examples)
lr.minimize().run()
self.assertAllClose(0.395226, unregularized_loss.eval(), rtol=3e-2, atol=3e-2)
self.assertAllClose(0.657446, loss.eval(), rtol=3e-2, atol=3e-2)
predicted_labels = tf.cast(tf.greater_equal(prediction, tf.ones_like(prediction) * 0.5), tf.float32)
self.assertAllClose([0, 1, 1, 1], predicted_labels.eval())
def testDenseFeaturesWeightedExamples(self):
with self._single_threaded_test_session():
examples = make_dense_examples_dict(
dense_feature_values=[[1.0, 1.0], [0.5, -0.5]],
weights=[3.0, 1.0],
labels=[1.0, 0.0])
variables = make_dense_variable_dict(2)
options = dict(symmetric_l2_regularization=1.0,
symmetric_l1_regularization=0,
loss_type='hinge_loss')
model = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
predictions = model.predictions(examples)
binary_predictions = get_binary_predictions_for_hinge(predictions)
train_op = model.minimize()
for _ in xrange(_MAX_ITERATIONS):
train_op.run()
# Point (1.0, 0.5) has higher weight than (1.0, -0.5) so the model will
# try to increase the margin from (1.0, 0.5). Due to regularization,
# (1.0, -0.5) will be within the margin. For these points and example
# weights, the optimal weights are w_1~=0.4 and w_2~=1.2 which give an L2
# loss of 0.5 * 0.25 * 0.25 * 1.6 = 0.2. The binary predictions will be
# correct, but the boundary will be much closer to the 2nd point than the
# first one.
self.assertAllClose([1.0, -0.2], predictions.eval(), atol=0.05)
self.assertAllEqual([1, 0], binary_predictions.eval())
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
self.assertAllClose(0.2, unregularized_loss.eval(), atol=0.02)
self.assertAllClose(0.4, regularized_loss.eval(), atol=0.02)
def testDenseFeaturesSeparableWithinMargins(self):
with self._single_threaded_test_session():
examples = make_dense_examples_dict(
dense_feature_values=[[1.0, 1.0], [0.5, -0.5]],
weights=[1.0, 1.0],
labels=[1.0, 0.0])
variables = make_dense_variable_dict(2)
options = dict(symmetric_l2_regularization=1.0,
symmetric_l1_regularization=0,
loss_type='hinge_loss')
model = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
predictions = model.predictions(examples)
binary_predictions = get_binary_predictions_for_hinge(predictions)
train_op = model.minimize()
for _ in xrange(_MAX_ITERATIONS):
train_op.run()
# (1.0, 0.5) and (1.0, -0.5) are separable by x-axis but the datapoints
# are within the margins so there is unregularized loss (1/2 per example).
# For these datapoints, optimal weights are w_1~=0.0 and w_2~=1.0 which
# gives an L2 loss of ~0.25.
self.assertAllClose([0.5, -0.5], predictions.eval(), rtol=0.05)
self.assertAllEqual([1, 0], binary_predictions.eval())
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
self.assertAllClose(0.5, unregularized_loss.eval(), atol=0.02)
self.assertAllClose(0.75, regularized_loss.eval(), atol=0.02)
def testDenseFeaturesPerfectlySeparable(self):
with self._single_threaded_test_session():
examples = make_dense_examples_dict(
dense_feature_values=[[1.0, 1.0], [1.0, -1.0]],
weights=[1.0, 1.0],
labels=[1.0, 0.0])
variables = make_dense_variable_dict(2)
options = dict(symmetric_l2_regularization=1.0,
symmetric_l1_regularization=0,
loss_type='hinge_loss')
model = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
predictions = model.predictions(examples)
binary_predictions = get_binary_predictions_for_hinge(predictions)
train_op = model.minimize()
for _ in xrange(_MAX_ITERATIONS):
train_op.run()
self.assertAllClose([1.0, -1.0], predictions.eval(), atol=0.05)
self.assertAllEqual([1, 0], binary_predictions.eval())
# (1.0, 1.0) and (1.0, -1.0) are perfectly separable by x-axis (that is,
# the SVM's functional margin >=1), so the unregularized loss is ~0.0.
# There is only loss due to l2-regularization. For these datapoints, it
# turns out that w_1~=0.0 and w_2~=1.0 which means that l2 loss is ~0.25.
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
self.assertAllClose(0.0, unregularized_loss.eval(), atol=0.02)
self.assertAllClose(0.25, regularized_loss.eval(), atol=0.02)
def testDenseFeaturesSeparableWithinMargins(self):
with self._single_threaded_test_session():
examples, variables = make_dense_examples_and_variables_dicts(
dense_features_values=[[[1.0, 0.5], [1.0, -0.5]]], weights=[1.0, 1.0], labels=[1.0, 0.0]
)
options = dict(symmetric_l2_regularization=1.0, symmetric_l1_regularization=0, loss_type="hinge_loss")
model = SdcaModel(examples, variables, options)
tf.initialize_all_variables().run()
predictions = model.predictions(examples)
binary_predictions = get_binary_predictions_for_hinge(predictions)
train_op = model.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
# (1.0, 0.5) and (1.0, -0.5) are separable by x-axis but the datapoints
# are within the margins so there is unregularized loss (1/2 per example).
# For these datapoints, optimal weights are w_1~=0.0 and w_2~=1.0 which
# gives an L2 loss of ~0.25.
self.assertAllClose([0.5, -0.5], predictions.eval(), rtol=0.05)
self.assertAllEqual([1, 0], binary_predictions.eval())
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
self.assertAllClose(0.5, unregularized_loss.eval(), atol=0.02)
self.assertAllClose(0.75, regularized_loss.eval(), atol=0.02)
def testSomeUnweightedExamples(self):
# Setup test data with 4 examples, but should produce the same
# results as testSimple.
example_protos = [
# Will be used.
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
# Will be ignored.
make_example_proto({
'age': [1],
'gender': [0]
}, 0),
# Will be used.
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
# Will be ignored.
make_example_proto({
'age': [1],
'gender': [0]
}, 1),
]
example_weights = [1.0, 0.0, 1.0, 0.0]
for num_shards in _SHARD_NUMBERS:
with self._single_threaded_test_session():
# Only use examples 0 and 2
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
lr = SdcaModel(CONTAINER,
examples,
variables,
options,
num_table_shards=num_shards)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
train_op = lr.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
self.assertAllClose(0.411608,
unregularized_loss.eval(),
atol=0.05)
self.assertAllClose(0.525457, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(
predictions)
self.assertAllClose([0, 1, 1, 1], predicted_labels.eval())
self.assertAllClose(0.01,
lr.approximate_duality_gap().eval(),
rtol=1e-2,
atol=1e-2)
def testDistributedSimple(self):
# Setup test data
example_protos = [
make_example_proto({'age': [0],
'gender': [0]}, 0),
make_example_proto({'age': [1],
'gender': [1]}, 1),
]
example_weights = [1.0, 1.0]
for num_shards in _SHARD_NUMBERS:
for num_loss_partitions in _NUM_LOSS_PARTITIONS:
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(
symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
loss_type='logistic_loss',
num_table_shards=num_shards,
num_loss_partitions=num_loss_partitions)
lr = SdcaModel(examples, variables, options)
tf.global_variables_initializer().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
self.assertAllClose(0.693147, unregularized_loss.eval())
self.assertAllClose(0.693147, loss.eval())
train_op = lr.minimize()
def Minimize():
with self._single_threaded_test_session():
for _ in range(_MAX_ITERATIONS):
train_op.run()
threads = []
for _ in range(num_loss_partitions):
threads.append(Thread(target=Minimize))
threads[-1].start()
for t in threads:
t.join()
lr.update_weights(train_op).run()
# The high tolerance in unregularized_loss comparisons is due to the
# fact that it's possible to trade off unregularized_loss vs.
# regularization and still have a sum that is quite close to the
# optimal regularized_loss value. SDCA's duality gap only ensures
# that the regularized_loss is within 0.01 of optimal.
# 0.525457 is the optimal regularized_loss.
# 0.411608 is the unregularized_loss at that optimum.
self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05)
self.assertAllClose(0.525457, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertTrue(lr.approximate_duality_gap().eval() < 0.02)
def testDistributedSimple(self):
# Setup test data
example_protos = [
make_example_proto({'age': [0],
'gender': [0]}, 0),
make_example_proto({'age': [1],
'gender': [1]}, 1),
]
example_weights = [1.0, 1.0]
for num_shards in _SHARD_NUMBERS:
for num_loss_partitions in _NUM_LOSS_PARTITIONS:
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(
symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
loss_type='logistic_loss',
num_table_shards=num_shards,
num_loss_partitions=num_loss_partitions)
lr = SdcaModel(examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
self.assertAllClose(0.693147, unregularized_loss.eval())
self.assertAllClose(0.693147, loss.eval())
train_op = lr.minimize()
def Minimize():
with self._single_threaded_test_session():
for _ in range(_MAX_ITERATIONS):
train_op.run()
threads = []
for _ in range(num_loss_partitions):
threads.append(Thread(target=Minimize))
threads[-1].start()
for t in threads:
t.join()
lr.update_weights(train_op).run()
# The high tolerance in unregularized_loss comparisons is due to the
# fact that it's possible to trade off unregularized_loss vs.
# regularization and still have a sum that is quite close to the
# optimal regularized_loss value. SDCA's duality gap only ensures
# that the regularized_loss is within 0.01 of optimal.
# 0.525457 is the optimal regularized_loss.
# 0.411608 is the unregularized_loss at that optimum.
self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05)
self.assertAllClose(0.525457, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertTrue(lr.approximate_duality_gap().eval() < 0.02)
def testImbalanced(self):
# Setup test data with 1 positive, and 3 negative examples.
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [2],
'gender': [0]
}, 0),
make_example_proto({
'age': [3],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [1.0, 1.0, 1.0, 1.0]
for num_shards in _SHARD_NUMBERS:
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(3, 1)
options = dict(symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
lr = SdcaModel(CONTAINER,
examples,
variables,
options,
num_table_shards=num_shards)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
train_op = lr.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
self.assertAllClose(0.226487 + 0.102902,
unregularized_loss.eval(),
atol=0.08)
self.assertAllClose(0.328394 + 0.131364,
loss.eval(),
atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(
predictions)
self.assertAllEqual([0, 0, 0, 1], predicted_labels.eval())
self.assertAllClose(0.01,
lr.approximate_duality_gap().eval(),
rtol=1e-2,
atol=1e-2)
def testSimpleNoL2(self):
# Same as test above (so comments from above apply) but without an L2.
# The algorithm should behave as if we have an L2 of 1 in optimization but
# 0 in regularized_loss.
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [1.0, 1.0]
for num_shards in _SHARD_NUMBERS:
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=0,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
lr = SdcaModel(CONTAINER,
examples,
variables,
options,
num_table_shards=num_shards)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
self.assertAllClose(0.693147, unregularized_loss.eval())
self.assertAllClose(0.693147, loss.eval())
train_op = lr.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
# There is neither L1 nor L2 loss, so regularized and unregularized
# losses should be exactly the same.
self.assertAllClose(0.40244,
unregularized_loss.eval(),
atol=0.01)
self.assertAllClose(0.40244, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(
predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertAllClose(0.01,
lr.approximate_duality_gap().eval(),
rtol=1e-2,
atol=1e-2)
def testImbalanced(self):
# Setup test data with 1 positive, and 3 negative examples.
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [2],
'gender': [0]
}, 0),
make_example_proto({
'age': [3],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [1.0, 1.0, 1.0, 1.0]
for num_shards in _SHARD_NUMBERS:
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(3, 1)
options = dict(
symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
num_table_shards=num_shards,
loss_type='logistic_loss')
lr = SdcaModel(examples, variables, options)
variables_lib.global_variables_initializer().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
train_op = lr.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
lr.update_weights(train_op).run()
self.assertAllClose(
0.226487 + 0.102902, unregularized_loss.eval(), atol=0.08)
self.assertAllClose(0.328394 + 0.131364, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 0, 0, 1], predicted_labels.eval())
self.assertAllClose(
0.0, lr.approximate_duality_gap().eval(), rtol=2e-2, atol=1e-2)
def testSimple(self):
# Setup test data
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(
symmetric_l2_regularization=1.0,
symmetric_l1_regularization=0,
loss_type='hinge_loss')
model = SdcaModel(examples, variables, options)
variables_lib.global_variables_initializer().run()
# Before minimization, the weights default to zero. There is no loss due
# to regularization, only unregularized loss which is 0.5 * (1+1) = 1.0.
predictions = model.predictions(examples)
self.assertAllClose([0.0, 0.0], predictions.eval())
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
self.assertAllClose(1.0, unregularized_loss.eval())
self.assertAllClose(1.0, regularized_loss.eval())
# After minimization, the model separates perfectly the data points. There
# are 4 sparse weights: 2 for age (say w1, w2) and 2 for gender (say w3
# and w4). Solving the system w1 + w3 = 1.0, w2 + w4 = -1.0 and minimizing
# wrt to \|\vec{w}\|_2, gives w1=w3=1/2 and w2=w4=-1/2. This gives 0.0
# unregularized loss and 0.25 L2 loss.
train_op = model.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
model.update_weights(train_op).run()
binary_predictions = get_binary_predictions_for_hinge(predictions)
self.assertAllEqual([-1.0, 1.0], predictions.eval())
self.assertAllEqual([0, 1], binary_predictions.eval())
self.assertAllClose(0.0, unregularized_loss.eval())
self.assertAllClose(0.25, regularized_loss.eval(), atol=0.05)
def testPartitionedPrimals(self):
# Setup test data
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [1.0, 1.0]
for num_shards in _SHARD_NUMBERS:
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1, partitioned=True)
options = dict(
symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
num_table_shards=num_shards,
loss_type='logistic_loss')
lr = SdcaModel(examples, variables, options)
variables_lib.global_variables_initializer().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
self.assertAllClose(0.693147, unregularized_loss.eval())
self.assertAllClose(0.693147, loss.eval())
train_op = lr.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
lr.update_weights(train_op).run()
# The high tolerance in unregularized_loss comparisons is due to the
# fact that it's possible to trade off unregularized_loss vs.
# regularization and still have a sum that is quite close to the
# optimal regularized_loss value. SDCA's duality gap only ensures that
# the regularized_loss is within 0.01 of optimal.
# 0.525457 is the optimal regularized_loss.
# 0.411608 is the unregularized_loss at that optimum.
self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.05)
self.assertAllClose(0.525457, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertAllClose(
0.01, lr.approximate_duality_gap().eval(), rtol=1e-2, atol=1e-2)
def testSimple(self):
# Setup test data
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 2),
]
example_weights = [100.0, 100.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(
symmetric_l2_regularization=1.0,
symmetric_l1_regularization=0,
loss_type='poisson_loss')
model = SdcaModel(examples, variables, options)
variables_lib.global_variables_initializer().run()
# Before minimization, the weights default to zero. There is no loss due
# to regularization, only unregularized loss which is 1 for each example.
predictions = model.predictions(examples)
self.assertAllClose([1.0, 1.0], predictions.eval())
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
approximate_duality_gap = model.approximate_duality_gap()
self.assertAllClose(1.0, unregularized_loss.eval())
self.assertAllClose(1.0, regularized_loss.eval())
# There are 4 sparse weights: 2 for age (say w1, w2) and 2 for gender
# (say w3 and w4). The minimization leads to:
# w1=w3=-1.96487, argmin of 100*(exp(2*w)-2*w*0)+w**2.
# w2=w4=0.345708, argmin of 100*(exp(2*w)-2*w*2)+w**2.
# This gives an unregularized loss of .3167 and .3366 with regularization.
train_op = model.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
model.update_weights(train_op).run()
self.assertAllClose([0.0196, 1.9965], predictions.eval(), atol=1e-4)
self.assertAllClose(0.3167, unregularized_loss.eval(), atol=1e-4)
self.assertAllClose(0.3366, regularized_loss.eval(), atol=1e-4)
self.assertAllClose(0., approximate_duality_gap.eval(), atol=1e-6)
def testSimple(self):
# Setup test data
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(
symmetric_l2_regularization=1.0,
symmetric_l1_regularization=0,
loss_type='hinge_loss')
model = SdcaModel(examples, variables, options)
variables_lib.global_variables_initializer().run()
# Before minimization, the weights default to zero. There is no loss due
# to regularization, only unregularized loss which is 0.5 * (1+1) = 1.0.
predictions = model.predictions(examples)
self.assertAllClose([0.0, 0.0], predictions.eval())
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
self.assertAllClose(1.0, unregularized_loss.eval())
self.assertAllClose(1.0, regularized_loss.eval())
# After minimization, the model separates perfectly the data points. There
# are 4 sparse weights: 2 for age (say w1, w2) and 2 for gender (say w3
# and w4). Solving the system w1 + w3 = 1.0, w2 + w4 = -1.0 and minimizing
# wrt to \|\vec{w}\|_2, gives w1=w3=1/2 and w2=w4=-1/2. This gives 0.0
# unregularized loss and 0.25 L2 loss.
train_op = model.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
model.update_weights(train_op).run()
binary_predictions = get_binary_predictions_for_hinge(predictions)
self.assertAllEqual([-1.0, 1.0], predictions.eval())
self.assertAllEqual([0, 1], binary_predictions.eval())
self.assertAllClose(0.0, unregularized_loss.eval())
self.assertAllClose(0.25, regularized_loss.eval(), atol=0.05)
def testSomeUnweightedExamples(self):
# Setup test data with 4 examples, but should produce the same
# results as testSimple.
example_protos = [
# Will be used.
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
# Will be ignored.
make_example_proto({
'age': [1],
'gender': [0]
}, 0),
# Will be used.
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
# Will be ignored.
make_example_proto({
'age': [1],
'gender': [0]
}, 1),
]
example_weights = [1.0, 0.0, 1.0, 0.0]
with self.test_session(use_gpu=False):
# Only use examples 0 and 2
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(examples, 1, 1)
options = dict(symmetric_l2_regularization=0.25,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
tf.initialize_all_variables().run()
lr = SdcaModel(examples, variables, options)
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
prediction = lr.predictions(examples)
lr.minimize().run()
self.assertAllClose(0.395226, unregularized_loss.eval())
self.assertAllClose(0.526336, loss.eval())
predicted_labels = tf.cast(
tf.greater_equal(prediction,
tf.ones_like(prediction) * 0.5), tf.float32)
self.assertAllClose([0, 1, 1, 1], predicted_labels.eval())
def testSimpleLogistic(self):
# Setup test data
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
lr = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
self.assertAllClose(0.693147, unregularized_loss.eval())
self.assertAllClose(0.693147, loss.eval())
for _ in xrange(5):
lr.minimize().run()
# The high tolerance in unregularized_loss comparisons is due to the
# fact that it's possible to trade off unregularized_loss vs.
# regularization and still have a sum that is quite close to the
# optimal regularized_loss value. SDCA's duality gap only ensures that
# the regularized_loss is within 0.01 of optimal.
# 0.525457 is the optimal regularized_loss.
# 0.411608 is the unregularized_loss at that optimum.
self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.11)
self.assertAllClose(0.525457, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertAllClose(0.01,
lr.approximate_duality_gap().eval(),
rtol=1e-2,
atol=1e-2)
def testSimple(self):
# Setup test data
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1.0,
symmetric_l1_regularization=0,
loss_type='smooth_hinge_loss')
model = SdcaModel(examples, variables, options)
tf.initialize_all_variables().run()
# Before minimization, the weights default to zero. There is no loss due
# to regularization, only unregularized loss which is 0.5 * (1+1) = 1.0.
predictions = model.predictions(examples)
self.assertAllClose([0.0, 0.0], predictions.eval())
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
self.assertAllClose(1.0, unregularized_loss.eval())
self.assertAllClose(1.0, regularized_loss.eval())
# After minimization, the model separates perfectly the data points. There
# are 4 sparse weights: 2 for age (say w1, w2) and 2 for gender (say w3
# and w4). The minimization leads to w1=w3=1/3 and w2=w4=-1/3. This gives
# an unregularized hinge loss of 0.33 and a 0.11 L2 loss
train_op = model.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
binary_predictions = get_binary_predictions_for_hinge(predictions)
self.assertAllClose([-0.67, 0.67], predictions.eval(), atol=0.05)
self.assertAllEqual([0, 1], binary_predictions.eval())
self.assertAllClose(0.33, unregularized_loss.eval(), atol=0.02)
self.assertAllClose(0.44, regularized_loss.eval(), atol=0.02)
def testImbalancedWithExampleWeights(self):
# Setup test data with 1 positive, and 1 negative example.
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [3.0, 1.0]
for num_shards in _SHARD_NUMBERS:
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
num_table_shards=num_shards,
loss_type='logistic_loss')
lr = SdcaModel(examples, variables, options)
variables_lib.global_variables_initializer().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
train_op = lr.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
lr.update_weights(train_op).run()
self.assertAllClose(0.284860,
unregularized_loss.eval(),
atol=0.08)
self.assertAllClose(0.408044, loss.eval(), atol=0.012)
predicted_labels = get_binary_predictions_for_logistic(
predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertAllClose(0.0,
lr.approximate_duality_gap().eval(),
rtol=2e-2,
atol=1e-2)
def testSimple(self):
# Setup test data
example_protos = [
make_example_proto({'age': [0],
'gender': [0]}, 0),
make_example_proto({'age': [1],
'gender': [1]}, 1),
]
example_weights = [1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(
symmetric_l2_regularization=1.0,
symmetric_l1_regularization=0,
loss_type='smooth_hinge_loss')
model = SdcaModel(examples, variables, options)
tf.initialize_all_variables().run()
# Before minimization, the weights default to zero. There is no loss due
# to regularization, only unregularized loss which is 0.5 * (1+1) = 1.0.
predictions = model.predictions(examples)
self.assertAllClose([0.0, 0.0], predictions.eval())
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
self.assertAllClose(1.0, unregularized_loss.eval())
self.assertAllClose(1.0, regularized_loss.eval())
# After minimization, the model separates perfectly the data points. There
# are 4 sparse weights: 2 for age (say w1, w2) and 2 for gender (say w3
# and w4). The minimization leads to w1=w3=1/3 and w2=w4=-1/3. This gives
# an unregularized hinge loss of 0.33 and a 0.11 L2 loss
train_op = model.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
model.update_weights(train_op).run()
binary_predictions = get_binary_predictions_for_hinge(predictions)
self.assertAllClose([-0.67, 0.67], predictions.eval(), atol=0.05)
self.assertAllEqual([0, 1], binary_predictions.eval())
self.assertAllClose(0.33, unregularized_loss.eval(), atol=0.02)
self.assertAllClose(0.44, regularized_loss.eval(), atol=0.02)
def testInstancesOfOneClassOnly(self):
# Setup test data with 1 positive (ignored), and 1 negative example.
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [0]
}, 1), # Shares gender with the instance above.
]
example_weights = [1.0, 0.0] # Second example "omitted" from training.
for num_shards in _SHARD_NUMBERS:
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1,
symmetric_l1_regularization=0,
num_table_shards=num_shards,
loss_type='logistic_loss')
lr = SdcaModel(examples, variables, options)
variables_lib.global_variables_initializer().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
train_op = lr.minimize()
for _ in range(_MAX_ITERATIONS):
train_op.run()
lr.update_weights(train_op).run()
self.assertAllClose(0.411608,
unregularized_loss.eval(),
atol=0.05)
self.assertAllClose(0.525457, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(
predictions)
self.assertAllEqual([0, 0], predicted_labels.eval())
self.assertAllClose(0.01,
lr.approximate_duality_gap().eval(),
rtol=1e-2,
atol=1e-2)
def testSomeUnweightedExamples(self):
# Setup test data with 4 examples, but should produce the same
# results as testSimple.
example_protos = [
# Will be used.
make_example_proto(
{'age': [0],
'gender': [0]}, 0),
# Will be ignored.
make_example_proto(
{'age': [1],
'gender': [0]}, 0),
# Will be used.
make_example_proto(
{'age': [1],
'gender': [1]}, 1),
# Will be ignored.
make_example_proto(
{'age': [1],
'gender': [0]}, 1),
]
example_weights = [1.0, 0.0, 1.0, 0.0]
with self.test_session(use_gpu=False):
# Only use examples 0 and 2
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(examples, 1, 1)
options = dict(symmetric_l2_regularization=0.25,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
tf.initialize_all_variables().run()
lr = SdcaModel(examples, variables, options)
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
prediction = lr.predictions(examples)
lr.minimize().run()
self.assertAllClose(0.395226, unregularized_loss.eval())
self.assertAllClose(0.526336, loss.eval())
predicted_labels = tf.cast(
tf.greater_equal(prediction,
tf.ones_like(prediction) * 0.5), tf.float32)
self.assertAllClose([0, 1, 1, 1], predicted_labels.eval())
def testSimpleLogisticNoL2(self):
# Same as test above (so comments from above apply) but without an L2.
# The algorithm should behave as if we have an L2 of 1 in optimization but
# 0 in regularized_loss.
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=0,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
lr = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
self.assertAllClose(0.693147, unregularized_loss.eval())
self.assertAllClose(0.693147, loss.eval())
for _ in xrange(5):
lr.minimize().run()
self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.11)
self.assertAllClose(0.371705, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertAllClose(0.01,
lr.approximate_duality_gap().eval(),
rtol=1e-2,
atol=1e-2)
def testImbalanced(self):
# Setup test data with 1 positive, and 3 negative examples.
example_protos = [
make_example_proto(
{'age': [0],
'gender': [0]}, 0),
make_example_proto(
{'age': [2],
'gender': [0]}, 0),
make_example_proto(
{'age': [3],
'gender': [0]}, 0),
make_example_proto(
{'age': [1],
'gender': [1]}, 1),
]
example_weights = [1.0, 1.0, 1.0, 1.0]
config = tf.ConfigProto(inter_op_parallelism_threads=1,
intra_op_parallelism_threads=1)
with self.test_session(use_gpu=False, config=config):
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(3, 1)
options = dict(symmetric_l2_regularization=0.25,
symmetric_l1_regularization=0,
loss_type='logistic_loss',
prior=-1.09861)
tf.initialize_all_variables().run()
lr = SdcaModel(CONTAINER, examples, variables, options)
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
prediction = lr.predictions(examples)
lr.minimize().run()
self.assertAllClose(0.331710, unregularized_loss.eval(),
rtol=3e-2, atol=3e-2)
self.assertAllClose(0.591295, loss.eval(), rtol=3e-2, atol=3e-2)
predicted_labels = tf.cast(
tf.greater_equal(prediction,
tf.ones_like(prediction) * 0.5), tf.float32)
self.assertAllEqual([0, 0, 0, 1], predicted_labels.eval())
def testSimpleNoL2(self):
# Same as test above (so comments from above apply) but without an L2.
# The algorithm should behave as if we have an L2 of 1 in optimization but
# 0 in regularized_loss.
example_protos = [
make_example_proto(
{'age': [0],
'gender': [0]}, 0),
make_example_proto(
{'age': [1],
'gender': [1]}, 1),
]
example_weights = [1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=0,
symmetric_l1_regularization=0,
loss_type='logistic_loss')
lr = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
self.assertAllClose(0.693147, unregularized_loss.eval())
self.assertAllClose(0.693147, loss.eval())
for _ in xrange(5):
lr.minimize().run()
self.assertAllClose(0.411608, unregularized_loss.eval(), rtol=0.11)
self.assertAllClose(0.371705, loss.eval(), atol=0.01)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertAllClose(0.01,
lr.approximate_duality_gap().eval(),
rtol=1e-2,
atol=1e-2)
def testImbalanced(self):
# Setup test data with 1 positive, and 3 negative examples.
example_protos = [
make_example_proto(
{'age': [0],
'gender': [0]}, 0),
make_example_proto(
{'age': [2],
'gender': [0]}, 0),
make_example_proto(
{'age': [3],
'gender': [0]}, 0),
make_example_proto(
{'age': [1],
'gender': [1]}, 1),
]
example_weights = [1.0, 1.0, 1.0, 1.0]
with self._single_threaded_test_session():
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(3, 1)
options = dict(symmetric_l2_regularization=0.25,
symmetric_l1_regularization=0,
loss_type='logistic_loss',
prior=-1.09861)
tf.initialize_all_variables().run()
lr = SdcaModel(CONTAINER, examples, variables, options)
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
prediction = lr.predictions(examples)
lr.minimize().run()
self.assertAllClose(0.331710, unregularized_loss.eval(),
rtol=3e-2, atol=3e-2)
self.assertAllClose(0.591295, loss.eval(), rtol=3e-2, atol=3e-2)
predicted_labels = tf.cast(
tf.greater_equal(prediction,
tf.ones_like(prediction) * 0.5), tf.float32)
self.assertAllEqual([0, 0, 0, 1], predicted_labels.eval())
def testSimple(self):
# Setup test data
example_protos = [
make_example_proto({"age": [0], "gender": [0]}, 0),
make_example_proto({"age": [1], "gender": [1]}, 1),
]
example_weights = [1.0, 1.0]
with self.test_session(use_gpu=False):
examples = make_example_dict(example_protos, example_weights)
variables = make_variable_dict(1, 1)
options = dict(symmetric_l2_regularization=1.0, symmetric_l1_regularization=0, loss_type="hinge_loss")
model = SdcaModel(CONTAINER, examples, variables, options)
tf.initialize_all_variables().run()
# Before minimization, the weights default to zero. There is no loss due
# to regularization, only unregularized loss which is 0.5 * (1+1) = 1.0.
predictions = model.predictions(examples)
self.assertAllClose([0.0, 0.0], predictions.eval())
unregularized_loss = model.unregularized_loss(examples)
regularized_loss = model.regularized_loss(examples)
self.assertAllClose(1.0, unregularized_loss.eval())
self.assertAllClose(1.0, regularized_loss.eval())
# After minimization, the model separates perfectly the data points. There
# are 4 sparse weights: 2 for age (say w1, w2) and 2 for gender (say w3
# and w4). Solving the system w1 + w3 = 1.0, w2 + w4 = -1.0 and minimizing
# wrt to \|\vec{w}\|_2, gives w1=w3=1/2 and w2=w4=-1/2. This gives 0.0
# unregularized loss and 0.25 L2 loss.
for _ in xrange(5):
model.minimize().run()
binary_predictions = get_binary_predictions_for_hinge(predictions)
self.assertAllEqual([-1.0, 1.0], predictions.eval())
self.assertAllEqual([0.0, 1.0], binary_predictions.eval())
self.assertAllClose(0.0, unregularized_loss.eval())
self.assertAllClose(0.25, regularized_loss.eval(), atol=0.05)
def testDistributedSimple(self):
# Distributed SDCA may not converge if the workers update concurrently the
# same example. In this test the examples are partitioned across workers.
# The examples are the same for all workers, just the example_ids are
# different.
example_protos = [
make_example_proto({
'age': [0],
'gender': [0]
}, 0),
make_example_proto({
'age': [1],
'gender': [1]
}, 1),
]
example_weights = [1.0, 1.0]
examples = make_example_dict(example_protos, example_weights)
example_ids = array_ops.placeholder(
dtypes.string, shape=(len(example_weights),))
examples['example_ids'] = example_ids
variables = make_variable_dict(1, 1)
for num_shards in _SHARD_NUMBERS:
for num_loss_partitions in _NUM_LOSS_PARTITIONS:
with self._single_threaded_test_session():
options = dict(
# Keep the same solution as for TestSimple: since the number of
# examples is multplied by num_loss_partitions, multiply also
# L2 by the same value.
symmetric_l2_regularization=num_loss_partitions,
symmetric_l1_regularization=0,
loss_type='logistic_loss',
num_table_shards=num_shards,
num_loss_partitions=num_loss_partitions)
lr = SdcaModel(examples, variables, options)
variables_lib.global_variables_initializer().run()
unregularized_loss = lr.unregularized_loss(examples)
loss = lr.regularized_loss(examples)
predictions = lr.predictions(examples)
self.assertAllClose(0.693147, unregularized_loss.eval())
self.assertAllClose(0.693147, loss.eval())
train_op = lr.minimize()
def minimize(worker_id):
with self._single_threaded_test_session():
feed_dict = {example_ids: [
str(i + worker_id*len(example_weights)) for i in range(
len(example_weights))]}
for _ in range(_MAX_ITERATIONS):
train_op.run(feed_dict=feed_dict) # pylint: disable=cell-var-from-loop
threads = []
for worker_id in range(num_loss_partitions):
threads.append(threading.Thread(target=minimize, args=(worker_id,)))
threads[-1].start()
for t in threads:
t.join()
lr.update_weights(train_op).run(feed_dict={
example_ids: [str(i) for i in range(len(example_weights))]})
# Test only the unregularized loss because the optimal value of the
# regularized loss depends on num_loss_partitions.
self.assertAllClose(0.411608, unregularized_loss.eval(), atol=0.02)
predicted_labels = get_binary_predictions_for_logistic(predictions)
self.assertAllEqual([0, 1], predicted_labels.eval())
self.assertNear(0.0, lr.approximate_duality_gap().eval(), 0.02)
